Data for the paper "Global Analyses of Human Immune Variation Reveal Baseline Predictors of Postvaccination Responses" published in Cell in 2014 (Q6684129)

This dataset includes ready for the analysis flow cytomety, microarray, microneutralization titer and elispot data used in the paper. https://www.cell.com/cell/fulltext/S0092-8674(14)00406-1 If you use this data for your work please cite the following publication: Tsang JS, Schwartzberg PL, Kotliarov Y, Biancotto A, Xie Z, Germain RN, Wang E, Olnes MJ, Narayanan M, Golding H, Moir S, Dickler HB, Perl S, Cheung F; Baylor HIPC Center; CHI Consortium. Global analyses of human immune variation reveal baseline predictors of postvaccination responses. Cell. 2014 Apr 10;157(2):499-513. doi: 10.1016/j.cell.2014.03.031. Abstract A major goal of systems biology is the development of models that accurately predict responses to perturbation. Constructing such models requires the collection of dense measurements of system states, yet transformation of data into predictive constructs remains a challenge. To begin to model human immunity, we analyzed immune parameters in depth both at baseline and in response to influenza vaccination. Peripheral blood mononuclear cell transcriptomes, serum titers, cell subpopulation frequencies, and B cell responses were assessed in 63 individuals before and after vaccination and were used to develop a systematic framework to dissect inter- and intra-individual variation and build predictive models of postvaccination antibody responses. Strikingly, independent of age and pre-existing antibody titers, accurate models could be constructed using pre-perturbation cell populations alone, which were validated using independent baseline time points. Most of the parameters contributing to prediction delineated temporally stable baseline differences across individuals, raising the prospect of immune monitoring before intervention. Please contact Dr. John Tsang (john.tsang@yale.edu) for questions regarding this data.